CN113240453A - Commodity sales dynamic pushing management system based on block chain - Google Patents
Commodity sales dynamic pushing management system based on block chain Download PDFInfo
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Abstract
The invention discloses a commodity sales dynamic promotion management system based on a block chain, which comprises a data layer, a data middle layer, a decision distribution layer and a user layer, wherein the data middle layer mainly comprises a commodity sales tracking module and a sales data preprocessing module; the decision distribution layer comprises a matching calibration module, a sales purchase evolution module, an optimization prediction replenishment module and a dynamic pushing sales module. The invention can accurately screen the two-stage double purchasing matching degree of each purchasing user by analyzing the purchasing users purchasing the same commodity type and each commodity under the same commodity type, improves the accuracy of the matching degree analysis among the purchasing users, obtains the sales promotion prediction supply coefficient through the prediction supply model to realize the distributed dynamic promotion management of the commodities, expands the customers to be promoted, increases the efficiency of commodity promotion, realizes the optimized management of targeted commodity promotion sales, and reduces the interference of promotion to the users with small purchasing possibility.
Description
Technical Field
The invention belongs to the technical field of commodity sales promotion management, and relates to a block chain-based dynamic commodity sales promotion management system.
Background
The block chain is a shared database, the data or information stored in the shared database has the characteristics of 'unforgeability', 'whole-course trace', 'traceability', 'public transparency', 'collective maintenance' and the like, based on the characteristics, the block chain technology lays a solid 'trust' foundation, creates a reliable 'cooperation' mechanism, and has a wide application prospect, so that the block chain is applied to commodity sales management according to the characteristics of the block chain, and another mode of commodity sales management can be realized.
With the development of social science and technology, the life of people is greatly improved, online shopping is often adopted for people to buy, the problems of low push precision and overload of pushed commodities exist in the process of pushing commodities in the existing electronic commerce, so that a user frequently suffers from push interference of commodity advertisements in the purchasing process, meanwhile, the problem of low push accuracy exists in the existing commodity pushing process only by pushing according to the past purchase records, browsing records and the like of the user, the push interference caused by the user with low purchase probability is increased, in the existing commodity selling pushing process, the purchasing habit of each purchasing user can not be bound according to the fact that each purchasing user carries out double matching association on commodity types and commodities under the commodity types according to the past purchase records of each purchasing user, the matching analysis among the purchasing users is realized, in addition, the selling advancing process can not be carried out according to the error between the actual selling advancing degree and the standard selling advancing degree in the commodity selling process The prediction compensation is carried out, and further the commodity propulsion process cannot be dynamically optimized, so that the problems of poor optimization degree, poor pertinence, low propulsion efficiency and the like of commodity sales propulsion exist.
Disclosure of Invention
The invention aims to provide a block chain-based dynamic commodity sales promotion management system, which solves the problems in the background art.
The purpose of the invention can be realized by the following technical scheme:
the commodity sales dynamic propulsion management system based on the block chain comprises a data layer, a sales information acquisition terminal is used as the data layer, one node in the block chain is used for acquiring commodity sales data information, encoding and sorting the acquired commodity sales data information and uploading the encoded and sorted commodity sales data information to the block chain;
the data intermediate layer is used for carrying out sales tracking and preprocessing on the commodity sales data information acquired by each sales information acquisition terminal, acquiring the purchase accumulation degree coefficient of each purchasing user for purchasing the same commodity type, and publishing the purchase accumulation degree coefficient of each purchasing user for purchasing the same commodity type after preprocessing to the decision distribution layer;
and the decision distribution layer processes the purchase accumulation degree coefficient between the purchase user and the same commodity type after the pre-processing to obtain a matching similar calibration coefficient, analyzes the actual sales promotion coefficient between the purchase users corresponding to the matching similar calibration coefficient, analyzes the sales promotion prediction supply coefficient according to the actual sales promotion coefficient and the combined establishment prediction supply model, and dynamically performs distributed dynamic sales promotion on the commodity to be sold according to the sales promotion prediction supply coefficient.
And the system further comprises a user layer which mainly comprises managers, wherein the managers can increase the number of the sales information acquisition terminals, edit the management authority of each person, and increase or decrease the researched commodity types, the sales area range and the commodity warehousing basic information.
Further, the data middle layer mainly comprises a commodity sales tracking module and a sales data preprocessing module;
the commodity sales tracking module is used for acquiring the types of commodities purchased by each purchasing user in each sales area, the purchase quantity of each commodity under each commodity type, the sales evaluation content of each purchased commodity and the time for each purchasing user to purchase each commodity, and sending the acquired past purchase data information of each purchasing user in each sales area to the sales data preprocessing module;
the sales data preprocessing module is used for receiving past purchase data information of each purchasing user in each sales area sent by the commodity sales tracking module, extracting past purchased commodity types in the past purchase data information of each purchasing user in the sales area and the time of each purchasing user for purchasing each commodity type, performing matching and overlapping processing, analyzing purchase accumulation degree coefficients among the same commodity types purchased by each purchasing user, performing association binding on each selected purchasing user with the purchase accumulation degree coefficient being greater than a set purchase accumulation degree coefficient threshold value, and sending each purchasing user with the associated purchase accumulation degree coefficient YB being greater than the set threshold value to the matching calibration module.
Further, the sales data preprocessing module performs matching superposition processing on each purchasing user, and comprises the following steps:
a step a1 of extracting past purchasing commodity types and time of purchasing each commodity type of each purchasing user P, wherein P is 1, 2.
Step A2, determining the times of each purchasing user repeatedly purchasing each commodity type according to the time of each purchasing user purchasing each commodity type;
step a3, the purchase percentage of each product type S (S1, 2.., S) is determined one by one
CpsShowing the times of the p-th purchasing user purchasing the s-th commodity category, and screening out the commodity category with the largest purchasing ratio;
step a4, sequentially extracting the purchasing users with the largest purchasing proportion rate, and counting the purchasing accumulation degree coefficient YB ═ 1+ λ) e of the purchasing users with the largest purchasing proportion rate who purchase the same kind of goods with the quantity greater than U timesG -UG represents the number of purchasing the same commodity type by each purchasing user with the largest purchasing percentage, U is the set number of purchasing the same commodity type and is a known numerical value, and lambda is a scale factor and takes a value of 1.24;
step A5, judging whether the purchase accumulation degree coefficient YB counted in the step A4 is larger than a set purchase accumulation degree coefficient threshold, if so, sequentially reducing the U value until the purchase accumulation degree coefficient YB is larger than the set purchase accumulation degree coefficient threshold, and associating the purchase users among the purchase users, the purchase accumulation degree coefficient YB of which is larger than the set purchase accumulation degree coefficient threshold;
and step A6, successively screening the maximum purchase occupation ratio of the rest commercial varieties, and executing the steps A4-A5 until the purchase occupation ratio is smaller than a set purchase occupation ratio threshold value.
Further, the decision distribution layer comprises a matching calibration module, a sales purchase evolution module, an optimization prediction replenishment module and a dynamic pushing sales module;
the matching calibration module is used for receiving each purchasing user of which the associated and bound purchasing accumulation coefficient sent by the sales data preprocessing module is greater than the set purchasing accumulation coefficient threshold, and extracts the sales evaluation content of each purchasing user with the purchasing accumulation coefficient larger than the set purchasing accumulation coefficient threshold value to each commodity in the same commodity category, comparing the sale evaluation content with the keyword set corresponding to each purchase satisfaction degree, counting the purchase satisfaction degree coefficient, and the purchase satisfaction degree grade and each purchase satisfaction weight coefficient of the purchase user to each commodity under the same commodity type are screened out through the purchase satisfaction coefficient, meanwhile, the matching calibration module analyzes matching similar calibration coefficients among purchasing users of which the associated and bound purchasing accumulation coefficient is greater than a set threshold value according to the purchasing satisfaction degree grade and the purchasing satisfaction weight coefficient of each commodity under the same purchased commodity type;
the dynamic propulsion evolution module is used for receiving the matching similar calibration coefficients among the purchasing users sent by the matching calibration module, screening out the maximum matching similar calibration coefficients among the purchasing users, respectively extracting all commodities under various commodity types purchased by the two purchasing users and the time for purchasing all commodities under various commodity types, which correspond to the maximum matching similar calibration coefficients, counting the actual sales propulsion coefficients of the subsequent purchasing users according to the purchasing sequence, and sending the actual sales propulsion coefficients to the optimized prediction replenishment module;
the optimization prediction replenishment module is used for screening out standard sales promotion evaluation coefficients corresponding to the matched similar calibration coefficients, receiving actual sales promotion coefficients sent by the dynamic promotion evolution module, performing optimization prediction on sales promotion of purchasing users by establishing a prediction replenishment model to obtain sales promotion prediction replenishment coefficients, and sending the sales promotion prediction replenishment coefficients after optimization prediction to the dynamic promotion sales module;
the dynamic propelling and selling module is used for acquiring the optimized and predicted selling propelling and predicting supply coefficient, extracting the data information of the commodities to be sold stored in each block chain node, and performing distributed dynamic selling propelling on the commodities by combining the optimized and predicted selling propelling and predicting supply coefficient and the data information of the commodities to be sold.
Further, the matching calibration module calculates a matching similarity calibration coefficient between the purchase users of which the associated purchase accumulation degree coefficient is greater than a set threshold value according to a formula
βQThe types of the commodities which are jointly purchased by the purchasing users Q with the associated binding purchase accumulation coefficient larger than the set purchase accumulation coefficient threshold value are represented, i and j respectively belong to one of the purchasing users Q with the purchase accumulation coefficient larger than the set threshold value,
denoted as the kth item in the s-th item category,
expressed as a purchase satisfaction weight coefficient for the ith purchasing user for the kth item in the item category s,
expressed as a purchase satisfaction weight coefficient for the kth item in the item category s for the jth purchasing user,
and
belonging to one of alpha 1, alpha 2, alpha 3, alpha 4, alpha 5, alpha 6,
expressed as the average purchase satisfaction level of all the purchasing users Q whose purchase accumulation degree coefficient is greater than the set threshold value for the k-th commodity under the commodity category s,
expressed as the purchase satisfaction level of the ith purchasing user for the kth commodity under the commodity category s,
expressed as the purchase satisfaction level of the jth purchasing user for the kth commodity under the commodity category s,
and
one of values respectively belonging to W1, W2, W3, W4, W5 and W6;
the satisfaction degree coefficients corresponding to the purchase satisfaction degree grades are L0-L1, L1-L2, L2-L3, L3-L4, L4-L5 and L5-L6 respectively, the purchase satisfaction degree grades are W1, W2, W3, W4, W5 and W6 respectively, different purchase satisfaction degree grades correspond to different satisfaction weight coefficients, the satisfaction weight coefficients are respectively alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 and alpha 6, and the value of 0 is more than alpha 1 and less than alpha 2 and less than alpha 3 and less than alpha 4 and less than alpha 5 and less than alpha 6 and less than 1.
Further, the dynamic pushing selling module carries out distributed dynamic pushing on the commodity, and adopts the following dynamic pushing method:
step R1, acquiring data information of commodities to be sold in each area block node to acquire the quantity to be sold and the time to be sold of each commodity under each commodity type in each area;
step R3, screening out commodity prediction replenishment propulsion frequency according to the sales propulsion prediction replenishment coefficient, and carrying out commodity pushing on purchasing users with matching similar calibration coefficients larger than a set matching similar calibration coefficient threshold value Z according to the epithelial prediction replenishment propulsion frequency;
step R4, counting the sales volume advanced by the predicted replenishment propulsion frequency of the commodity in step R3, and determining whether the sales speed of the sales volume advanced by the predicted replenishment propulsion frequency of the commodity is greater than a set speed to be sold, where the speed to be sold is the ratio of the number of the commodities to be sold to the time to be sold;
and R5, if the sales speed is less than the set selling speed, reducing the matched similar calibration coefficient threshold value Z at equal intervals, expanding the customers to be promoted to increase the efficiency of commodity propulsion, and repeatedly executing the steps R3-R5 until the selling of the commodities to be sold is completed within the selling time.
The invention has the beneficial effects that:
according to the dynamic commodity sales promotion management system based on the block chain, commodity sales tracking, sales data preprocessing and other processing are carried out on commodity sales data information of each node in the block chain, the purchase accumulation degree coefficient among all purchasing users can be analyzed, and then all purchasing users larger than a set threshold value are related and bound according to the purchase accumulation degree coefficient, so that the purchasing users with the purchasing habits of the same commodity type are effectively classified and divided primarily, and reliable data are provided for the analysis of the sales satisfaction degree of all commodities in all commodity types in the later period.
The method and the device analyze the purchase satisfaction degree grade and the purchase satisfaction weight coefficient of each purchasing user on each commodity through extracting the sale evaluation content of each bound purchasing user on each commodity under the same commodity type, and analyze the matching similar calibration coefficient of each purchasing user on each commodity under the same commodity type by combining the calculation formula of the matching similar calibration coefficient, wherein the purchase accumulation degree coefficient of the associated binding is larger than the set threshold value, so as to analyze the specific matching degree of each commodity under the same commodity type, realize the accurate screening of the two-stage double purchase matching degree and improve the accuracy of the analysis of the matching degree between the purchasing users.
The invention analyzes the sales promotion prediction replenishment coefficient by establishing the prediction replenishment model, visually represents the difference between the actual sales promotion coefficient and the standard sales promotion evaluation coefficient corresponding to the matched similar calibration coefficient, and carries out distributed dynamic promotion management on the commodities according to the sales promotion prediction replenishment coefficient so as to improve the promotion efficiency and the accuracy of commodity sales, expand the customers to be promoted and increase the efficiency of commodity promotion, realize the optimized management of targeted commodity promotion sales, and reduce the purchase interference caused by dynamic commodity promotion to the users with small purchase possibility.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The commodity sales dynamic promotion management system based on the block chain comprises a sales information acquisition terminal, wherein the sales information acquisition terminal is used as a data layer to acquire commodity sales data information, and it needs to be noted that the sales information acquisition terminal is a node in the block chain and is used as a process of the node in the block chain, namely the sales information acquisition terminal acquires the commodity sales data information and encodes and sorts the acquired commodity sales data information and uploads the encoded and sorted commodity sales data information to the block chain, wherein the commodity sales data information mainly comprises the types of commodities sold, the sales volume of each commodity under each commodity type, the sales speed, commodity sales evaluation content, commodity sales region information and the like.
Data intermediate layer, can be understood as: the method comprises the steps of carrying out sales tracking and preprocessing on commodity sales data information acquired by each sales information acquisition terminal, acquiring purchase accumulation degree coefficients of purchasing users for purchasing the same commodity types, and publishing the purchase accumulation degree coefficients of the purchasing users for purchasing the same commodity types to a decision distribution layer after preprocessing, so that the acquired commodity sales data information is conveniently subjected to resource arrangement, and correlated binding is carried out on purchasing users with purchase accumulation, and the sales core data information is enabled to realize efficient operation and information sharing.
Decision distribution layer, which can be understood as: the method comprises the steps of processing purchase accumulation degree coefficients of the same commodity types purchased by each preprocessed purchasing user to obtain matching similar calibration coefficients, improving accuracy and precision of the similarity degree of the same commodity purchased by a statistical user, carrying out actual sales promotion coefficient statistics among the purchasing users corresponding to the matching similar calibration coefficients, analyzing sales promotion prediction supply coefficients by establishing a prediction supply model, reducing the fact that the actual sales promotion coefficients are far away from standard sales promotion evaluation coefficients due to insufficient sales promotion, and finally carrying out distributed dynamic sales promotion on commodities to be sold according to the sales promotion prediction supply coefficients dynamically, so that dynamic promotion is carried out on commodity sales, dynamic sales of the commodities are promoted, rationalization of commodity sales promotion is improved, commodity sales is promoted, and satisfaction of promoted purchasing users is improved.
The user layer mainly comprises management personnel, the management personnel can add the number of the sales information acquisition terminals and edit the management authority of each personnel, the types of the researched commodities, the sales area range, the commodity storage basic information and the like can be increased or reduced, and the commodity storage basic information comprises commodity storage capacity, purchase period, storage duration and the like.
Particularly, in a dynamic commodity sales promotion management system based on a regional chain, each layer forming a block chain is respectively composed of different functional modules. Namely, the data layer adopts a plurality of sales information acquisition terminals, and the sales information acquisition terminals are used for acquiring sales volume of each commodity type, sales regions of each commodity type and sales evaluation contents of each commodity type.
The data middle layer mainly comprises a commodity sales tracking module and a sales data preprocessing module.
Taking the sale of snack foods as an example, the snack foods are classified into different snack commodity types, and the same snack commodity type has various substitutes due to different manufacturers, such as: soda, cookies, bread, candied fruits, nuts, dairy products and the like, and various soda beverages in the soda can be used as substitutes.
The commodity sales tracking module is used for acquiring the types of commodities purchased by each purchasing user in each sales area in the past, the purchase quantity of each commodity under each commodity type, the sales evaluation content of each purchased commodity and the time of each purchasing user for purchasing each commodity, and sending the acquired past purchase data information of each purchasing user in each sales area to the sales data preprocessing module.
The sales data preprocessing module is used for receiving past purchase data information of each purchasing user in each sales area sent by the commodity sales tracking module, extracting past purchased commodity types in the past purchase data information of each purchasing user in the sales area and the time of each purchasing user for purchasing each commodity type, performing matching and overlapping processing, analyzing purchase accumulation degree coefficients among the same commodity types purchased by each purchasing user, performing association binding on each selected purchasing user with the purchase accumulation degree coefficient being greater than a set purchase accumulation degree coefficient threshold value, and sending each purchasing user with the associated purchase accumulation degree coefficient YB being greater than the set threshold value to the matching calibration module.
Each commodity under the same commodity type can be used as a mutual substitute, if the number of each purchasing user who is associated and bound when each purchasing user purchases the same commodity type and has a purchase accumulation degree coefficient larger than a set threshold value is Q, wherein the matching and overlapping processing of the sales data preprocessing module on each purchasing user comprises the following steps:
a step a1 of extracting past purchasing commodity types and time of purchasing each commodity type of each purchasing user P, wherein P is 1, 2.
Step A2, determining the times of each purchasing user repeatedly purchasing each commodity type according to the time of each purchasing user purchasing each commodity type;
step a3, the purchase percentage of each product type S (S1, 2.., S) is determined one by one
CpsShowing the times of the p-th purchasing user purchasing the s-th commodity category, and screening out the commodity category with the largest purchasing ratio;
step a4, sequentially extracting the purchasing users with the largest purchasing proportion rate, and counting the purchasing accumulation degree coefficient YB ═ 1+ λ) e of the purchasing users with the largest purchasing proportion rate who purchase the same kind of goods with the quantity greater than U timesG -UG is the number of the same kind of commodity purchased by each purchasing user with the largest purchase ratio, U is the set number of the same kind of commodity purchased, and is a known value, λ is a scale factor, and takes a value of 1.24, and the larger the purchase accumulation coefficient is, the larger the purchase habit of each purchasing user is.
Step A5, judging whether the purchase accumulation degree coefficient YB counted in the step A4 is larger than a set purchase accumulation degree coefficient threshold, if so, sequentially reducing the U value until the purchase accumulation degree coefficient YB is larger than the set purchase accumulation degree coefficient threshold, and associating the purchase users among the purchase users, the purchase accumulation degree coefficient YB of which is larger than the set purchase accumulation degree coefficient threshold;
and step A6, successively screening the maximum purchase occupation ratio of the rest commercial varieties, and executing the steps A4-A5 until the purchase occupation ratio is smaller than a set purchase occupation ratio threshold value.
The sales data preprocessing module can carry out matching, associating and binding on the same commodity type by each purchasing user, is convenient for screening out each purchasing user who has the purchasing accumulation coefficient of the same commodity type larger than the set purchasing accumulation coefficient threshold value from each purchasing user to carry out associating and binding, realizes effective classification and division of each purchasing user according to the purchasing habit, and provides reliable data for the analysis of the sales satisfaction degree of each commodity under the commodity type in the later period.
The decision distribution layer comprises a matching calibration module, a sales purchase evolution module, an optimization prediction replenishment module and a dynamic pushing sales module.
The matching calibration module is used for receiving each purchasing user of which the associated and bound purchasing accumulation degree coefficient is larger than the set purchasing accumulation degree coefficient threshold value and sent by the sales data preprocessing module, extracting the sales evaluation content of each purchasing user of which the purchasing accumulation degree coefficient is larger than the set purchasing accumulation degree coefficient threshold value to each commodity in the same commodity variety, comparing the sales evaluation content with the keyword set corresponding to each purchasing satisfaction degree grade, and counting the purchasing satisfaction degree coefficient
The larger the purchase satisfaction coefficient is, the higher the purchase satisfaction degree of the purchasing user is, and the purchase satisfaction degree grades and the purchase satisfaction weight coefficients of various commodities under the same commodity type purchased by the purchasing user are screened out through the purchase satisfaction coefficient, wherein the keyword set V (V) corresponding to the purchase satisfaction degree gradesn1,vn2,...,vnf,...,vnN)，vnf represents the f-th keyword corresponding to the nth purchase satisfaction level,v′npfskshowing whether the f-th keyword corresponding to the n-th purchase satisfaction grade exists in the evaluation content of the k-th product in the s-th product category of the p-th purchasing user, and if so, showing v'npfsThe value is equal to the natural number e, otherwise, the value is 1, chiskp is expressed as a purchase satisfaction coefficient of the pth purchasing user to the kth commodity in the s commodity category, n belongs to the numerical values from W1 to W6, and meanwhile, the matching calibration module analyzes the matching similarity calibration coefficient among the purchasing users of which the associated and bound purchase accumulation coefficient is larger than a set threshold value according to the purchase satisfaction grades of the commodities under the same purchased commodity category and the purchase satisfaction weight coefficient
The larger the matching similarity calibration coefficient is, the larger the similarity goodness of fit between purchasing users of which the purchasing accumulation coefficient bound by the association is larger than a set threshold value is, the two-stage double screening of the purchasing matching degree is realized, namely, the purchasing commodity type and the specific commodity under the purchasing commodity type are respectively realized, the matching similarity calibration of the specific commodity under the same commodity type is guaranteed under the screening of the purchasing same commodity type, the accurate analysis and statistics of the matching degree between the purchasing users are improved, and betaQThe types of the commodities which are jointly purchased by the purchasing users Q with the associated binding purchase accumulation coefficient larger than the set purchase accumulation coefficient threshold value are represented, i and j respectively belong to one of the purchasing users Q with the purchase accumulation coefficient larger than the set threshold value,
denoted as the kth item in the s-th item category,
expressed as a purchase satisfaction weight coefficient for the ith purchasing user for the kth item in the item category s,
denoted as the jth purchaseThe user is satisfied with the purchase of the kth item in the item category s by the weight coefficient,
and
belonging to one of alpha 1, alpha 2, alpha 3, alpha 4, alpha 5, alpha 6,
expressed as the average purchase satisfaction level of all the purchasing users Q whose purchase accumulation degree coefficient is greater than the set threshold value for the k-th commodity under the commodity category s,
expressed as the purchase satisfaction level of the ith purchasing user for the kth commodity under the commodity category s,
expressed as the purchase satisfaction level of the jth purchasing user for the kth commodity under the commodity category s,
and
each belonging to one of the values W1, W2, W3, W4, W5 and W6.
The satisfaction degree coefficients corresponding to the purchase satisfaction degree grades are L0-L1, L1-L2, L2-L3, L3-L4, L4-L5 and L5-L6 respectively, the purchase satisfaction degree grades are W1, W2, W3, W4, W5 and W6 respectively, different purchase satisfaction degree grades correspond to different satisfaction weight coefficients, the satisfaction weight coefficients are respectively alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 and alpha 6, and the value of 0 is more than alpha 1 and less than alpha 2 and less than alpha 3 and less than alpha 4 and less than alpha 5 and less than alpha 6 and less than 1.
By comprehensively analyzing the purchase satisfaction degree grade and the purchase satisfaction weight coefficient of each purchased commodity under the same commodity type, the matching degree between the purchase users of which the associated and bound purchase accumulation degree coefficient is greater than a set threshold value can be analyzed, the comprehensive matching degree of each purchase user of which the associated and bound matching coincidence degree is greater than the set threshold value to the same commodity type is visually displayed, further matching screening and determination are realized, the problem that the comprehensive similarity coincidence degree of each purchase user to the same commodity type cannot be accurately evaluated due to the uneven distribution of each substitute in the same commodity type is solved, and the accuracy of the purchase matching coincidence analysis of each purchase user to the same commodity type is improved.
The dynamic propulsion evolution module is used for receiving the matching similar calibration coefficients among the purchasing users sent by the matching calibration module, screening the maximum matching similar calibration coefficients among the purchasing users, respectively extracting all commodities under various commodity types purchased by the two purchasing users corresponding to the maximum matching similar calibration coefficients and the time for purchasing all commodities under various commodity types, sequencing according to the purchasing sequence, dynamically pushing the purchased commodities with the purchasing sequence being in the first order to the purchasing users in the next order, and counting the actual sales propulsion coefficients according to the purchasing conditions of the purchasing users in the next order
D is expressed by the number of the commodities purchased by the purchasing users who purchase later and the commodities purchased by the purchasing users who purchase earlier actually overlap with each other with the time point of t1 as a starting point, e is expressed by a natural number, gamma is expressed by a sales influence coefficient of mutual purchase interference, and takes a value of 1.692, D is expressed by the time point of t1 as a cut-off point, the same number of purchased goods by the later-purchased user as the previously-purchased user, H is represented by a point of time t1 as a cutoff point, the total quantity of the commodities purchased by the user who purchases the commodities in advance and sends an actual sales promotion coefficient to the optimization prediction replenishment module, wherein the actual sales promotion coefficient is used for expressing the synchronization degree of the commodities purchased by the user who purchases the commodities in advance and the commodities purchased by the user who purchases the commodities in advance, and then showing the consistency of the purchasing user corresponding to the maximum matching similar calibration coefficient on the subsequent purchased commodities.
The optimization prediction replenishment module is used for screening out standard pins corresponding to the matched similar calibration coefficientsSales promotion evaluation coefficient phiSign boardAnd receiving the actual sales promotion coefficient sent by the dynamic promotion evolution module, carrying out optimization prediction on the sales promotion of the purchasing user by establishing a prediction replenishment model to obtain a sales promotion prediction replenishment coefficient eta, and establishing a prediction replenishment model
The optimized and predicted sales promotion prediction replenishment coefficient is sent to the dynamic promotion sales module, the difference between the actual sales promotion coefficient and the standard sales promotion evaluation coefficient can be visually displayed according to the sales prediction replenishment coefficient, so that a reliable commodity promotion parameter basis can be provided for promotion in the commodity sales process in the later period, the prediction replenishment amount required by the commodity sales of a purchasing user in the commodity purchasing process can be accurately predicted, the maximization of the sales in the commodity sales process can be ensured and the workload required by the commodity sales promotion is minimum by promoting the commodities according to the sales promotion prediction replenishment coefficient, the optimization of commodity promotion management is realized, the commodity sales amount can be improved, and the interference caused by repeatedly promoting the commodities to the non-purchasing user can be reduced.
The dynamic propelling and selling module is used for acquiring the optimized and predicted selling propelling and predicting supply coefficient, extracting the data information of the commodities to be sold stored in each block chain node, and performing distributed dynamic propelling on the commodities by combining the optimized and predicted selling propelling and predicting supply coefficient and the data information of the commodities to be sold so as to improve the propelling efficiency of commodity selling.
The dynamic propelling sale module carries out distributed dynamic propelling on the commodity, and adopts the following dynamic propelling method:
step R1, acquiring data information of commodities to be sold in each area block node to acquire the quantity to be sold and the time to be sold of each commodity under each commodity type in each area;
step R3, screening out commodity prediction replenishment propulsion frequency according to the sales propulsion prediction replenishment coefficient, and carrying out commodity pushing on purchasing users with the matching similar calibration coefficient larger than a set matching similar calibration coefficient threshold value Z according to the epithelial prediction replenishment propulsion frequency, wherein different sales propulsion prediction replenishment coefficients correspond to different commodity prediction replenishment propulsion frequencies, namely the commodity prediction replenishment propulsion frequency is equal to the product of the sales propulsion prediction replenishment coefficient of the commodity and the commodity propulsion frequency in the past;
step R4, counting the sales volume advanced by the predicted replenishment propulsion frequency of the commodity in step R3, and determining whether the sales speed of the sales volume advanced by the predicted replenishment propulsion frequency of the commodity is greater than a set speed to be sold, where the speed to be sold is the ratio of the number of the commodities to be sold to the time to be sold;
and R5, if the sales speed is less than the set selling speed, reducing the matched similar calibration coefficient threshold value Z at equal intervals, expanding the customers to be promoted to increase the efficiency of commodity propulsion, and repeatedly executing the steps R3-R5 until the selling of the commodities to be sold is completed within the selling time.
The method comprises the steps of comprehensively analyzing data information of commodities to be sold stored in a block chain and the sales promotion prediction replenishment coefficient to obtain a commodity prediction replenishment promotion frequency corresponding to the sales promotion prediction replenishment coefficient, realizing that the commodities to be sold are subjected to commodity promotion to purchasing users with matching similar calibration coefficients larger than a set matching similar calibration coefficient threshold in a commodity prediction replenishment promotion frequency mode, and dynamically reducing the matching similar calibration coefficient threshold in combination with actual commodity sales to expand customers to be promoted, so that the efficiency of commodity promotion is increased, targeted commodity promotion sales management is realized, and purchasing interference caused by dynamic commodity promotion to users with low purchasing possibility is reduced.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (7)
1. Commodity sales developments impel management system based on block chain, its characterized in that: the system comprises a data layer, a sales information acquisition terminal is used as the data layer, a node in a block chain is provided, the sales information acquisition terminal acquires commodity sales data information, and the acquired commodity sales data information is encoded, sorted and uploaded to the block chain;
the data intermediate layer is used for carrying out sales tracking and preprocessing on the commodity sales data information acquired by each sales information acquisition terminal, acquiring the purchase accumulation degree coefficient of each purchasing user for purchasing the same commodity type, and publishing the purchase accumulation degree coefficient of each purchasing user for purchasing the same commodity type after preprocessing to the decision distribution layer;
and the decision distribution layer processes the purchase accumulation degree coefficient between the purchase user and the same commodity type after the pre-processing to obtain a matching similar calibration coefficient, analyzes the actual sales promotion coefficient between the purchase users corresponding to the matching similar calibration coefficient, analyzes the sales promotion prediction supply coefficient according to the actual sales promotion coefficient and the combined establishment prediction supply model, and dynamically performs distributed dynamic sales promotion on the commodity to be sold according to the sales promotion prediction supply coefficient.
2. The block chain based dynamic commodity sales advancement management system of claim 1, wherein: the system also comprises a user layer which mainly comprises managers, wherein the managers can add the number of the sales information acquisition terminals, edit the management authority of each manager, and increase or decrease the types of the researched commodities, the sales area range and the basic information of commodity storage.
3. The block chain based dynamic commodity sales advancement management system of claim 1, wherein: the data intermediate layer mainly comprises a commodity sales tracking module and a sales data preprocessing module;
the commodity sales tracking module is used for acquiring the types of commodities purchased by each purchasing user in each sales area, the purchase quantity of each commodity under each commodity type, the sales evaluation content of each purchased commodity and the time for each purchasing user to purchase each commodity, and sending the acquired past purchase data information of each purchasing user in each sales area to the sales data preprocessing module;
the sales data preprocessing module is used for receiving past purchase data information of each purchasing user in each sales area sent by the commodity sales tracking module, extracting past purchased commodity types in the past purchase data information of each purchasing user in the sales area and the time of each purchasing user for purchasing each commodity type, performing matching and overlapping processing, analyzing purchase accumulation degree coefficients among the same commodity types purchased by each purchasing user, performing association binding on each selected purchasing user with the purchase accumulation degree coefficient being greater than a set purchase accumulation degree coefficient threshold value, and sending each purchasing user with the associated purchase accumulation degree coefficient YB being greater than the set threshold value to the matching calibration module.
4. The block chain based dynamic commodity sales advancement management system of claim 3, wherein: the sales data preprocessing module carries out matching and overlapping processing on each purchasing user and comprises the following steps:
a step a1 of extracting past purchasing commodity types and time of purchasing each commodity type of each purchasing user P, wherein P is 1, 2.
Step A2, determining the times of each purchasing user repeatedly purchasing each commodity type according to the time of each purchasing user purchasing each commodity type;
step a3, the purchase percentage of each product type S (S1, 2.., S) is determined one by one
CpsShowing the times of the p-th purchasing user purchasing the s-th commodity category, and screening out the commodity category with the largest purchasing ratio;
step a4, sequentially extracting the purchasing users with the largest purchasing proportion rate, and counting the purchasing accumulation degree coefficient YB ═ 1+ λ) e of the purchasing users with the largest purchasing proportion rate who purchase the same kind of goods with the quantity greater than U timesG-UG represents the number of the same kind of commodities purchased by each purchasing user with the largest purchase ratio, and U represents the set purchaseThe quantity of the same commodity type is a known value, and the value of lambda is 1.24, wherein lambda is a scale factor;
step A5, judging whether the purchase accumulation degree coefficient YB counted in the step A4 is larger than a set purchase accumulation degree coefficient threshold, if so, sequentially reducing the U value until the purchase accumulation degree coefficient YB is larger than the set purchase accumulation degree coefficient threshold, and associating the purchase users among the purchase users, the purchase accumulation degree coefficient YB of which is larger than the set purchase accumulation degree coefficient threshold;
and step A6, successively screening the maximum purchase occupation ratio of the rest commercial varieties, and executing the steps A4-A5 until the purchase occupation ratio is smaller than a set purchase occupation ratio threshold value.
5. The block chain based dynamic commodity sales advancement management system of claim 1, wherein: the decision distribution layer comprises a matching calibration module, a sales purchase evolution module, an optimization prediction replenishment module and a dynamic promotion sales module;
the matching calibration module is used for receiving each purchasing user of which the associated and bound purchasing accumulation coefficient sent by the sales data preprocessing module is greater than the set purchasing accumulation coefficient threshold, and extracts the sales evaluation content of each purchasing user with the purchasing accumulation coefficient larger than the set purchasing accumulation coefficient threshold value to each commodity in the same commodity category, comparing the sale evaluation content with the keyword set corresponding to each purchase satisfaction degree, counting the purchase satisfaction degree coefficient, and the purchase satisfaction degree grade and each purchase satisfaction weight coefficient of the purchase user to each commodity under the same commodity type are screened out through the purchase satisfaction coefficient, meanwhile, the matching calibration module analyzes matching similar calibration coefficients among purchasing users of which the associated and bound purchasing accumulation coefficient is greater than a set threshold value according to the purchasing satisfaction degree grade and the purchasing satisfaction weight coefficient of each commodity under the same purchased commodity type;
the dynamic propulsion evolution module is used for receiving the matching similar calibration coefficients among the purchasing users sent by the matching calibration module, screening out the maximum matching similar calibration coefficients among the purchasing users, respectively extracting all commodities under various commodity types purchased by the two purchasing users and the time for purchasing all commodities under various commodity types, which correspond to the maximum matching similar calibration coefficients, counting the actual sales propulsion coefficients of the subsequent purchasing users according to the purchasing sequence, and sending the actual sales propulsion coefficients to the optimized prediction replenishment module;
the optimization prediction replenishment module is used for screening out standard sales promotion evaluation coefficients corresponding to the matched similar calibration coefficients, receiving actual sales promotion coefficients sent by the dynamic promotion evolution module, performing optimization prediction on sales promotion of purchasing users by establishing a prediction replenishment model to obtain sales promotion prediction replenishment coefficients, and sending the sales promotion prediction replenishment coefficients after optimization prediction to the dynamic promotion sales module;
the dynamic propelling and selling module is used for acquiring the optimized and predicted selling propelling and predicting supply coefficient, extracting the data information of the commodities to be sold stored in each block chain node, and performing distributed dynamic selling propelling on the commodities by combining the optimized and predicted selling propelling and predicting supply coefficient and the data information of the commodities to be sold.
6. The block chain based dynamic commodity sales advancement management system of claim 5, wherein: the matching calibration module calculates the matching similar calibration coefficient between the purchase users of which the associated and bound purchase accumulation coefficient is greater than the set threshold value according to the formula
βQThe types of the commodities which are jointly purchased by the purchasing users Q with the associated binding purchase accumulation coefficient larger than the set purchase accumulation coefficient threshold value are represented, i and j respectively belong to one of the purchasing users Q with the purchase accumulation coefficient larger than the set threshold value,
denoted as the kth item in the s-th item category,
expressed as a purchase satisfaction weight coefficient for the ith purchasing user for the kth item in the item category s,
expressed as a purchase satisfaction weight coefficient for the kth item in the item category s for the jth purchasing user,
and
belonging to one of alpha 1, alpha 2, alpha 3, alpha 4, alpha 5, alpha 6,
expressed as the average purchase satisfaction level of all the purchasing users Q whose purchase accumulation degree coefficient is greater than the set threshold value for the k-th commodity under the commodity category s,
expressed as the purchase satisfaction level of the ith purchasing user for the kth commodity under the commodity category s,
expressed as the purchase satisfaction level of the jth purchasing user for the kth commodity under the commodity category s,
and
one of values respectively belonging to W1, W2, W3, W4, W5 and W6;
the satisfaction degree coefficients corresponding to the purchase satisfaction degree grades are L0-L1, L1-L2, L2-L3, L3-L4, L4-L5 and L5-L6 respectively, the purchase satisfaction degree grades are W1, W2, W3, W4, W5 and W6 respectively, different purchase satisfaction degree grades correspond to different satisfaction weight coefficients, the satisfaction weight coefficients are respectively alpha 1, alpha 2, alpha 3, alpha 4, alpha 5 and alpha 6, and the value of 0 is more than alpha 1 and less than alpha 2 and less than alpha 3 and less than alpha 4 and less than alpha 5 and less than alpha 6 and less than 1.
7. The block chain based dynamic commodity sales advancement management system of claim 5, wherein: the dynamic propelling sale module carries out distributed dynamic propelling on the commodity, and adopts the following dynamic propelling method:
step R1, acquiring data information of commodities to be sold in each area block node to acquire the quantity to be sold and the time to be sold of each commodity under each commodity type in each area;
step R3, screening out commodity prediction replenishment propulsion frequency according to the sales propulsion prediction replenishment coefficient, and carrying out commodity pushing on purchasing users with matching similar calibration coefficients larger than a set matching similar calibration coefficient threshold value Z according to the epithelial prediction replenishment propulsion frequency;
step R4, counting the sales volume advanced by the predicted replenishment propulsion frequency of the commodity in step R3, and determining whether the sales speed of the sales volume advanced by the predicted replenishment propulsion frequency of the commodity is greater than a set speed to be sold, where the speed to be sold is the ratio of the number of the commodities to be sold to the time to be sold;
and R5, if the sales speed is less than the set selling speed, reducing the matched similar calibration coefficient threshold value Z at equal intervals, expanding the customers to be promoted to increase the efficiency of commodity propulsion, and repeatedly executing the steps R3-R5 until the selling of the commodities to be sold is completed within the selling time.
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